想要了解图或图神经网络？没有比看论文更好的方式，面试阿里国际站运营一般会问什么

正文

• Paper：https://arxiv.org/abs/1805.10988
  
• Python Reference：https://github.com/SeongokRyu/Molecular-GAT
  
• Compound-protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 2018)
  
• Masashi Tsubaki, Kentaro Tomii, and Jun Sese
  
• Paper：https://academic.oup.com/bioinformatics/article/35/2/309/5050020
  
• Python Reference：https://github.com/masashitsubaki/CPI_prediction
  
• Python Reference：https://github.com/masashitsubaki/GNN_molecules
  
• Python Alternative：https://github.com/xnuohz/GCNDTI
  
• Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)
  
• Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes
  
• Paper：https://ieeexplore.ieee.org/abstract/document/8545310
  
• Python Reference：https://github.com/priba/siamese_ged
  
• Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)
  
• Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi
  
• Paper：https://arxiv.org/abs/1802.04944v1
  
• Python Reference：https://github.com/Luckick/EAGCN
  
• Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)
  
• Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu and Xiaoyan Zhu
  
• Paper：http://coai.cs.tsinghua.edu.cn/hml/media/files/2018_commonsense_ZhouHao_3_TYVQ7Iq.pdf
  
• Python Reference：https://github.com/tuxchow/ccm
  
• Residual Gated Graph ConvNets (ICLR 2018)
  
• Xavier Bresson and Thomas Laurent
  
• Paper：https://arxiv.org/pdf/1711.07553v2.pdf
  
• Python Pytorch Reference：https://github.com/xbresson/spatial_graph_convnets
  
• An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)
  
• Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen
  
• Paper：https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf
  
• Python Tensorflow Reference：https://github.com/muhanzhang/DGCNN
  
• Python Pytorch Reference：https://github.com/muhanzhang/pytorch_DGCNN
  
• MATLAB Reference：https://github.com/muhanzhang/DGCNN
  
• Python Alternative：https://github.com/leftthomas/DGCNN
  
• Python Alternative：https://github.com/hitlic/DGCNN-tensorflow
  
• SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)
  
• Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller
  
• Paper：https://arxiv.org/abs/1807.02839
  
• Python Reference：http://mott.in/publications/others/sgr/
  
• Deep Learning with Topological Signatures (NIPS 2017)
  
• Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl
  
• paper：https://arxiv.org/abs/1707.04041
  
• Python Reference：https://github.com/c-hofer/nips2017
  
• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)
  
• Martin Simonovsky and Nikos Komodakis
  
• paper：https://arxiv.org/pdf/1704.02901v3.pdf
  
• Python Reference：https://github.com/mys007/ecc
  
• Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)
  
• Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola
  
• Paper：https://arxiv.org/abs/1705.09037
  
• Python Reference：https://github.com/taolei87/icml17_knn
  
• Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)
  
• Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur
  
• Paper：https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks
  
• Python Reference：https://github.com/fouticus/pipgcn
  
• Graph Classification with 2D Convolutional Neural Networks (2017)
  
• Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis
  
• Paper：https://arxiv.org/abs/1708.02218
  
• Python Reference：https://github.com/Tixierae/graph_2D_CNN
  
• CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)
  
• Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
  
• Paper：https://arxiv.org/pdf/1705.07664v2.pdf
  
• Python Reference：https://github.com/fmonti/CayleyNet
  
• Semi-supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)
  
• Hai Nguyen, Shin-ichi Maeda, Kenta Oono
  
• Paper：https://arxiv.org/pdf/1711.10168.pdf
  
• Python Reference：https://github.com/pfnet-research/hierarchical-molecular-learning
  
• Kernel Graph Convolutional Neural Networks (2017)
  
• Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis
  
• Paper：https://arxiv.org/pdf/1710.10689.pdf
  
• Python Reference：https://github.com/giannisnik/cnn-graph-classification
  
• Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)
  
• Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough
  
• Paper：https://ieeexplore.ieee.org/document/7840988/
  
• Python Reference：https://github.com/sbonner0/DeepTopologyClassification
  
• Learning Convolutional Neural Networks for Graphs (ICML 2016)
  
• Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
  
• Paper：https://arxiv.org/abs/1605.05273
  
• Python Reference：https://github.com/tvayer/PSCN
  
• Gated Graph Sequence Neural Networks (ICLR 2016)
  
• Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
  
• Paper：https://arxiv.org/abs/1511.05493
  
• Python TensorFlow：https://github.com/bdqnghi/ggnn.tensorflow
  
• Python PyTorch：https://github.com/JamesChuanggg/ggnn.pytorch
  
• Python Reference：https://github.com/YunjaeChoi/ggnnmols
  
• Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)
  
• David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams
  
• Paper：https://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf
  
• Python Reference：https://github.com/fllinares/neural_fingerprints_tf
  
• Python Reference：https://github.com/jacklin18/neural-fingerprint-in-GNN
  
• Python Reference：https://github.com/HIPS/neural-fingerprint
  
• Python Reference：https://github.com/debbiemarkslab/neural-fingerprint-theano
  

Graph Kernels

• Message Passing Graph Kernels (2018)
  
• Giannis Nikolentzos, Michalis Vazirgiannis
  
• Paper：https://arxiv.org/pdf/1808.02510.pdf
  
• Python Reference：https://github.com/giannisnik/message_passing_graph_kernels
  
• Matching Node Embeddings for Graph Similarity (AAAI 2017)
  
• Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis
  
• Paper：https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494
  
• Global Weisfeiler-Lehman Graph Kernels (2017)
  
• Christopher Morris, Kristian Kersting and Petra Mutzel
  
• Paper：https://arxiv.org/pdf/1703.02379.pdf
  
• C++ Reference：https://github.com/chrsmrrs/glocalwl
  
• On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)
  
• Nils Kriege, Pierre-Louis Giscard, Richard Wilson
  
• Paper：https://arxiv.org/pdf/1606.01141.pdf
  
• Java Reference：https://github.com/nlskrg/optimal_assignment_kernels
  
• Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)
  
• Stephen Bonner, John Brennan, and A. Stephen McGough
  
• Paper：http://dro.dur.ac.uk/19773/1/19773.pdf?DDD10+lzdh59+d700tmt
  
• python Reference：https://github.com/sbonner0/GraphFingerprintComparison
  
• The Multiscale Laplacian Graph Kernel (NIPS 2016)
  
• Risi Kondor and Horace Pan
  
• Paper：https://arxiv.org/abs/1603.06186
  
• C++ Reference：https://github.com/horacepan/MLGkernel
  
• Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)
  
• Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel
  
• Paper：https://arxiv.org/abs/1610.00064
  
• Python Reference：https://github.com/chrsmrrs/hashgraphkernel
  
• Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)
  
• Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian
  
• Paper：https://link.springer.com/article/10.1007/s10994-015-5517-9
  
• Matlab Reference：https://github.com/marionmari/propagation_kernels
  
• Halting Random Walk Kernels (NIPS 2015)
  
• Mahito Sugiyama and Karsten M. Borgward
  
• Paper：https://pdfs.semanticscholar.org/79ba/8bcfbf9496834fdc22a1f7c96d26d776cd6c.pdf
  
• C++ Reference：https://github.com/BorgwardtLab/graph-kernels
  
• Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)
  
• Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt
  
• Paper：https://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf
  
• Subgraph Matching Kernels for Attributed Graphs (ICML 2012)
  
• Nils Kriege and Petra Mutzel
  
• Paper：https://arxiv.org/abs/1206.6483
  
• Python Reference：https://github.com/mockingbird2/GraphKernelBenchmark
  
• Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)
  
• Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang
  
• Paper：https://ieeexplore.ieee.org/document/6413884/
  
• Python Reference：https://github.com/benedekrozemberczki/NestedSubtreeHash
  
• Weisfeiler-Lehman Graph Kernels (JMLR 2011)
  
• Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
  
• Paper：http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
  
• Python Reference：https://github.com/jajupmochi/py-graph
  
• Python Reference：https://github.com/deeplego/wl-graph-kernels
  
• C++ Reference：https://github.com/BorgwardtLab/graph-kernels
  
• Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)
  
• Fabrizio Costa and Kurt De Grave
  
• Paper：https://icml.cc/Conferences/2010/papers/347.pdf
  
• C++ Reference：https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/www.bioinf.uni-freiburg.de/~costa/EDeNcpp.tgz
  
• Python Reference：https://github.com/fabriziocosta/EDeN
  
• A Linear-time Graph Kernel (ICDM 2009)
  
• Shohei Hido and Hisashi Kashima
  
• Paper：https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5360243
  
• Python Reference：https://github.com/hgascon/adagio
  
• Weisfeiler-Lehman Subtree Kernels (NIPS 2009)
  
• Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
  
• Paper：http://papers.nips.cc/paper/3813-fast-subtree-kernels-on-graphs.pdf
  
• Python Reference：https://github.com/jajupmochi/py-graph
  
• Python Reference：https://github.com/deeplego/wl-graph-kernels
  
• C++ Reference：https://github.com/BorgwardtLab/graph-kernels
  
• Fast Computation of Graph Kernels (NIPS 2006)
  
• S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph
  
• Paper：http://www.dbs.ifi.lmu.de/Publikationen/Papers/VisBorSch06.pdf
  
• Python Reference：https://github.com/jajupmochi/py-graph
  
• C++ Reference：https://github.com/BorgwardtLab/graph-kernels
  
• Shortest-Path Kernels on Graphs (ICDM 2005)
  
• Karsten M. Borgwardt and Hans-Peter Kriegel
  
• Paper：https://www.ethz.ch/content/dam/ethz/special-interest/bsse/borgwardt-lab/documents/papers/BorKri05.pdf
  
• C++ Reference：https://github.com/KitwareMedical/ITKTubeTK
  
• Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)
  
• Tamás Horváth, Thomas Gärtner, and Stefan Wrobel
  
• Paper：http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.6158&rep=rep1&type=pdf
  
• Python Reference：https://github.com/jajupmochi/py-graph
  
• Extensions of Marginalized Graph Kernels (ICML 2004)
  
• Pierre Mahe, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert
  
• Paper：http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf
  
• Python Reference：https://github.com/jajupmochi/py-graph
  
• Marginalized Kernels Between Labeled Graphs (ICML 2003)
  
• Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi
  
• Paper：https://pdfs.semanticscholar.org/2dfd/92c808487049ab4c9b45db77e9055b9da5a2.pdf
  
• Python Reference：https://github.com/jajupmochi/py-graph
  

不同时期的图建模

其实，我们可以将图建模分为图神经网络与传统的图模型。其中以前的图建模主要借助 Graph Embedding 为不同的节点学习低维向量表征，这借鉴了 NLP 中词嵌入的思想。而图神经网络借助深度学习进行更强大的图运算与图表征。

Graph Embedding 算法聚焦在如何对网络节点进行低维向量表示，相似的节点在表征空间中更加接近。相比之下，GNN 最大的优势在于它不只可以对一个节点进行语义表示。

例如 GNN 可以表示子图的语义信息，将网络中一小部分节点构成的语义表示出来，这是以前 Graph Embedding 不容易做到的。GNN 还可以在整个图网络上进行信息传播、聚合等建模，也就是说它可以把图网络当成一个整体进行建模。此外，GNN 对单个节点的表示也可以做得更好，因为它可以更好地建模周围节点丰富信息。

在传统图建模中，随机游走、最短路径等图方法会利用符号知识，但这些方法并没有办法很好地利用每个节点的语义信息。而深度学习技术更擅长处理非结构文本、图像等数据。简言之，我们可以将 GNN 看做将深度学习技术应用到符号表示的图数据上，或者说是从非结构化数据扩展到了结构化数据。GNN 能够充分融合符号表示和低维向量表示，发挥两者优势。

图建模论文与代码

在 的一项开源工作中，开发者收集了图建模相关的论文与实现，并且从经典的 Graph Embedding、Graph Kernel 到图神经网络都有涉及。它们在图嵌入、图分类、图表征等领域都是非常重要的论文。

项目地址：benedekrozemberczki/awesome-graph-classification

该项目主要收集的论文领域如下所示：

1. Factorization

2. Spectral and Statistical Fingerprints

3. Graph Neural Network

4. Graph Kernels

因式分解法

· Learning Graph Representation via Frequent Subgraphs (SDM 2018)

· Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung

· Paper：https://epubs.siam.org/doi/10.1137/1.9781611975321.35

· Python：nphdang/GE-FSG

· Anonymous Walk Embeddings (ICML 2018)

· Sergey Ivanov and Evgeny Burnaev

· Paper：https://arxiv.org/pdf/1805.11921.pdf

· Python：nd7141/AWE

· Graph2vec (MLGWorkshop 2017)

· Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan

· Paper：https://arxiv.org/abs/1707.05005

· Python High Performance：benedekrozemberczki/graph2vec

· Python Reference：MLDroid/graph2vec_tf

· Subgraph2vec (MLGWorkshop 2016)

· Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan

· Paper：https://arxiv.org/abs/1606.08928

· Python High Performance：MLDroid/subgraph2vec_gensim

· Python Reference：MLDroid/subgraph2vec_tf

· Rdf2Vec: RDF Graph Embeddings for Data Mining (ISWC 2016)

· Petar Ristoski and Heiko Paulheim

· Paper：https://link.springer.com/chapter/10.1007/978-3-319-46523-4_30

· Python Reference：airobert/RDF2VecAtWebScale

· Deep Graph Kernels (KDD 2015)

· Pinar Yanardag and S.V.N. Vishwanathan

· Paper：https://dl.acm.org/citation.cfm?id=2783417

· Python Reference：pankajk/Deep-Graph-Kernels

Spectral and Statistical Fingerprints

· A Simple Yet Effective Baseline for Non-Attribute Graph Classification (ICLR RLPM 2019)

· Chen Cai, Yusu Wang

· Paper：https://arxiv.org/abs/1811.03508

· Python Reference：Chen-Cai-OSU/LDP

· NetLSD (KDD 2018)

· Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller

· Paper：https://arxiv.org/abs/1805.10712

· Python Reference：xgfs/NetLSD

· A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning, NIPS 2018)

· Nathan de Lara and Edouard Pineau

· Paper：https://arxiv.org/pdf/1810.09155.pdf

· Python Reference：edouardpineau/A-simple-baseline-algorithm-for-graph-classification

· Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)